{"id":"W2622426673","doi":"10.1016/j.nucengdes.2017.06.006","title":"A thermoelectric generator based efficiency booster for performance enhancement of natural circulation systems","year":2017,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Booster (rocketry); Natural circulation; Thermoelectric generator; Performance enhancement; Thermoelectric effect; Materials science; Circulation (fluid dynamics); Generator (circuit theory); Engineering; Nuclear engineering; Automotive engineering; Mechanical engineering; Aerospace engineering; Physics; Medicine; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002817713,0.0001065311,0.0001661517,0.00004472099,0.000189813,0.0001231283,0.000138385,0.00003928017,0.00001796639],"category_scores_gemma":[0.00004620756,0.00008831947,0.00002288671,0.000029504,0.00002051771,0.0001391189,0.00001537899,0.00002898216,0.000004522574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001880903,"about_ca_system_score_gemma":0.0000156961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006641338,"about_ca_topic_score_gemma":3.770453e-8,"domain_scores_codex":[0.9993525,0.00001557542,0.0001689455,0.0001700502,0.0001084922,0.0001843996],"domain_scores_gemma":[0.9995194,0.00004826037,0.0001415904,0.0002072633,0.00005098206,0.00003249212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000593173,0.00001164218,0.000007400104,0.0001592066,0.000003567164,2.902526e-7,0.00003811485,0.01160631,0.9866057,0.0002884137,0.000009065046,0.001210997],"study_design_scores_gemma":[0.000264952,0.0001256295,0.0003563723,0.00004059099,0.00000796376,0.000001671828,0.000001996553,0.6171525,0.3817932,0.000005964745,0.0001547172,0.00009439761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8231477,0.0004066297,0.1757668,0.000007422682,0.0003227119,0.0002693729,0.000002265807,0.00003839727,0.00003869072],"genre_scores_gemma":[0.9931943,0.00001548849,0.006640923,0.00001452427,0.0000673715,0.00002649647,8.83799e-7,0.0000193177,0.0000206901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6055462,"threshold_uncertainty_score":0.3601563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470578597230105,"score_gpt":0.2163257087618203,"score_spread":0.2016199227895192,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}